Research On Defect Detection Method Of Thick Steel Shell Concrete Immersed Tunnel Based On Elastic Wave Field Characteristics

The inner structure of sandwich structure immersed tunnel is complex.In the process of self-compacting concrete pouring,it is easy to form a void defect between the steel plate and the concrete,which will affect the overall bearing capacity of the main structure of immersed tunnel.At present,there is no effective technical method to detect the pouring quality of steel shell concrete quickly,accurately and in a large range.This paper first through the full-size steel shell concrete immersed tube model conducted a prototype experiment to study the application of impact imaging method in non-destructive testing of Sandwich structure immersed tunnels(SSIT),and the criterion of void detection for impact imaging method was established.Secondly,a number of factors affecting the detection accuracy of the impact imaging method were analyzed by the finite element numerical analysis method.Then,based on the full-size prototype experiment data,the SVM and decision tree algorithm of machine learning were used to establish the void defect recognition model,and the advantages and disadvantages of different defect recognition models were compared and analyzed.Finally,based on a large number of detection results accumulated in practical engineering,the influence of different types of compartments on void defects and the areas prone to void defects in different compartments are analyzed,which can provide reference for void detection and optimization the design of compartments.The main research work and innovative achievements of this paper are as follows.1.Based on the propagation rules of elastic stress wave in different medium and proximal wave field theory,the experimental study on non-destructive test(NDT)of the S SIT structure was carried.The waveform amplitude characteristic,vibration response time,frequency spectrum characteristics were analyzed,and the corresponding relationship between the response waveform characteristics and the void defect was clarified.A comprehensive discriminant parameter----normalized impact response strength value was proposed to quantify the void height of the defect position,and the discriminant standard of the impact image method for void detection was determined.2.The numerical simulation analysis method was used to verify and extend the full-scale model experiment.By comparing the results of the numerical model under different working conditions,the influences of the thickness of the steel plate,material properties,sensor position and other factors on the impact image method were analyzed.The analysis results show that with the increase of steel plate thickness,the detection accuracy of this method decrease.At the same time,the location of geophone and the floating slurry layer on the concrete surface also have great influence on the detection result.3.Based on the data of full-scale model test,the feature of response waveform data and spectrum data was extracted,and the sample data database corresponding to the characteristic attributes of the wave data and the void condition was established.SVM and decision tree classification algorithm in machine learning algorithm were used to establish the void defect recognition model,and the accuracy of the model were better than the impact image method,and the accurate evaluation of the void defect height was realized.Through repeated test verification,the accuracy and robustness of the model are good,which is greatly improved compared with the impact image method,and realize the accurate evaluation of the void height of the defect.4.Based on the detection results of the five sandwich structure immersed tunnels in the Shenzhen-zhongshan passage project,the influence of the compartment size,Tstiffener structure,exhaust hole setting,casting hole setting and other factors on the occurrence of void defects were analyzed,and the areas prone to void defects in different type of compartment were analyzed.The statistical analysis results show that when the size of the compartment is 2.4m and 2.5m(B/L is about 0.8),the probability of void defect is the least.And the void defects are easy to appear on both sides of the T-stiffener and near the casting hole.Placing the pouring hole in the center of the compartment,reducing the T-stiffener structure and increasing the number of exhaust holes are beneficial to reduce the probability of void defects.